The present invention relates generally to promotion optimization methods and apparatus therefor. More particularly, the present invention relates to computer-implemented methods and computer-implemented apparatus for the generation of a batch of promotions in a design matrix with a recommendation overly for assisting users in the design of a promotional campaign.
Promotion refers to various practices designed to increase sales of a particular product or services and/or the profit associated with such sales. Generally speaking, the public often associates promotion with the sale of consumer goods and services, including consumer packaged goods (e.g., food, home and personal care), consumer durables (e.g., consumer appliances, consumer electronics, automotive leasing), consumer services (e.g., retail financial services, health care, insurance, home repair, beauty and personal care), and travel and hospitality (e.g., hotels, airline flights, and restaurants). Promotion is particularly heavily involved in the sale of consumer packaged goods (i.e., consumer goods packaged for sale to an end consumer). However, promotion occurs in almost any industry that offers goods or services to a buyer (whether the buyer is an end consumer or an intermediate entity between the producer and the end consumer).
The term promotion may refer to, for example, providing discounts (using for example a physical or electronic coupon or code) designed to, for example, promote the sales volume of a particular product or service. One aspect of promotion may also refer to the bundling of goods or services to create a more desirable selling unit such that sales volume may be improved. Another aspect of promotion may also refer to the merchandising design (with respect to looks, weight, design, color, etc.) or displaying of a particular product with a view to increasing its sales volume. It includes calls to action or marketing claims used in-store, on marketing collaterals, or on the package to drive demand. Promotions may be composed of all or some of the following: price based claims, secondary displays or aisle end-caps in a retail store, shelf signage, temporary packaging, placement in a retailer circular/flyer/coupon book, a colored price tag, advertising claims, or other special incentives intended to drive consideration and purchase behavior. These examples are meant to be illustrative and not limiting.
In discussing various embodiments of the present invention, the sale of consumer packaged goods (hereinafter “CPG”) is employed to facilitate discussion and ease of understanding. It should be kept in mind, however, that the promotion optimization methods and apparatuses discussed herein may apply to any industry in which promotion has been employed in the past or may be employed in the future.
Further, price discount is employed as an example to explain the promotion methods and apparatuses herein. It should be understood, however, that promotion optimization may be employed to manipulate factors other than price discount in order to influence the sales volume. An example of such other factors may include the call to action on a display or on the packaging, the size of the CPG item, the manner in which the item is displayed or promoted or advertised either in the store or in media, etc.
Generally speaking, it has been estimated that, on average, 17% of the revenue in the consumer packaged goods (CPG) industry is spent to fund various types of promotions, including discounts, designed to entice consumers to try and/or to purchase the packaged goods. In a typical example, the retailer (such as a grocery store) may offer a discount online or via a print circular to consumers. The promotion may be specifically targeted to an individual consumer (based on, for example, that consumer's demographics or past buying behavior). The discount may alternatively be broadly offered to the general public. Examples of promotions offered to general public include for example, a printed or electronic redeemable discount (e.g., coupon or code) for a specific CPG item. Another promotion example may include, for example, general advertising of the reduced price of a CPG item in a particular geographic area. Another promotion example may include in-store marking down of a particular CPG item only for a loyalty card user base.
In an example, if the consumer redeems the coupon or electronic code, the consumer is entitled to a reduced price for the CPG item. The revenue loss to the retailer due to the redeemed discount may be reimbursed, wholly or partly, by the manufacturer of the CPG item in a separate transaction.
Because promotion is expensive (in terms of, for example, the effort to conduct a promotion campaign and/or the per-unit revenue loss to the retailer/manufacturer when the consumer decides to take advantage of the discount), efforts are continually made to minimize promotion cost while maximizing the return on promotion dollars investment. This effort is known in the industry as promotion optimization.
For example, a typical promotion optimization method may involve examining the sales volume of a particular CPG item over time (e.g., weeks). The sales volume may be represented by a demand curve as a function of time, for example. A demand curve lift (excess over baseline) or dip (below baseline) for a particular time period would be examined to understand why the sales volume for that CPG item increases or decreases during such time period.
FIG. 1 shows an example demand curve 102 for Brand X cookies over some period of time. Two lifts 110 and 114 and one dip 112 in demand curve 102 are shown in the example of FIG. 1. Lift 110 shows that the demand for Brand X cookies exceeds the baseline at least during week 2. By examining the promotion effort that was undertaken at that time (e.g., in the vicinity of weeks 1-4 or week 2) for Brand X cookies, marketers have in the past attempted to judge the effectiveness of the promotion effort on the sales volume. If the sales volume is deemed to have been caused by the promotion effort and delivers certain financial performance metrics, that promotion effort is deemed to have been successful and may be replicated in the future in an attempt to increase the sales volume. On the other hand, dip 112 is examined in an attempt to understand why the demand falls off during that time (e.g., weeks 3 and 4 in FIG. 1). If the decrease in demand was due to the promotion in week 2 (also known as consumer pantry loading or retailer forward-buying, depending on whether the sales volume shown reflects the sales to consumers or the sales to retailers), this decrease in weeks 3 and 4 should be counted against the effectiveness of week 2.
One problem with the approach employed in the prior art has been the fact that the prior art approach is a backward-looking approach based on aggregate historical data. In other words, the prior art approach attempts to ascertain the nature and extent of the relationship between the promotion and the sales volume by examining aggregate data collected in the past. The use of historical data, while having some disadvantages (which are discussed later herein below), is not necessarily a problem. However, when such data is in the form of aggregate data (such as in simple terms of sales volume of Brand X cookies versus time for a particular store or geographic area), it is impossible to extract from such aggregate historical data all of the other factors that may more logically explain a particular lift or dip in the demand curve.
To elaborate, current promotion optimization approaches tend to evaluate sales lifts or dips as a function of four main factors: discount depth (e.g., how much was the discount on the CPG item), discount duration (e.g., how long did the promotion campaign last), timing (e.g., whether there was any special holidays or event or weather involved), and promotion type (e.g., whether the promotion was a price discount only, whether Brand X cookies were displayed/not displayed prominently, whether Brand X cookies were features/not featured in the promotion literature).
However, there may exist other factors that contribute to the sales lift or dip, and such factors are often not discoverable by examining, in a backward-looking manner, the historical aggregate sales volume data for Brand X cookies. This is because there is not enough information in the aggregate sales volume data to enable the extraction of information pertaining to unanticipated or seemingly unrelated events that may have happened during the sales lifts and dips and may have actually contributed to the sales lifts and dips.
Suppose, for example, that there was a discount promotion for Brand X cookies during the time when lift 110 in the demand curve 102 happens. However, during the same time, there was a breakdown in the distribution chain of Brand Y cookies, a competitor's cookies brand which many consumers view to be an equivalent substitute for Brand X cookies. With Brand Y cookies being in short supply in the store, many consumers bought Brand X instead for convenience sake. Aggregate historical sales volume data for Brand X cookies, when examined after the fact in isolation by Brand X marketing department thousands of miles away, would not uncover that fact. As a result, Brand X marketers may make the mistaken assumption that the costly promotion effort of Brand X cookies was solely responsible for the sales lift and should be continued, despite the fact that it was an unrelated event that contributed to most of the lift in the sales volume of Brand X cookies.
As another example, suppose, for example, that milk produced by a particular unrelated vendor was heavily promoted in the same grocery store or in a different grocery store nearby during the week that Brand X cookies experienced the sales lift 110. The milk may have been highlighted in the weekly circular, placed in a highly visible location in the store and/or a milk industry expert may have been present in the store to push buyers to purchase milk, for example. Many consumers ended up buying milk because of this effort whereas some of most of those consumers who bought during the milk promotion may have waited another week or so until they finished consuming the milk they bought in the previous weeks. Further, many of those milk-buying consumers during this period also purchased cookies out of an ingrained milk-and-cookies habit. Aggregate historical sales volume data for Brand X cookies would not uncover that fact unless the person analyzing the historical aggregate sales volume data for Brand X cookies happened to be present in the store during that week and had the insight to note that milk was heavily promoted that week and also the insight that increased milk buying may have an influence on the sales volume of Brand X cookies.
Software may try to take these unanticipated events into account but unless every SKU (stock keeping unit) in that store and in stores within commuting distance and all events, whether seemingly related or unrelated to the sales of Brand X cookies, are modeled, it is impossible to eliminate data noise from the backward-looking analysis based on aggregate historical sales data.
Even without the presence of unanticipated factors, a marketing person working for Brand X may be interested in knowing whether the relatively modest sales lift 114 comes from purchases made by regular Brand X cookies buyers or by new buyers being enticed by some aspect of the promotion campaign to buy Brand X cookies for the first time. If Brand X marketer can ascertain that most of the lift in sales during the promotion period that spans lift 114 comes from new consumers of Brand X cookies, such marketer may be willing to spend more money on the same type of sales promotion, even to the point of tolerating a negative ROI (return on investment) on his promotion dollars for this particular type of promotion since the recruitment of new buyers to a brand is deemed more much valuable to the company in the long run than the temporary increase in sales to existing Brand X buyers. Again, aggregate historical sales volume data for Brand X cookies, when examined in a backward-looking manner, would not provide such information.
Furthermore, even if all unrelated and related events and factors can be modeled, the fact that the approach is backward-looking means that there is no way to validate the hypothesis about the effect an event has on the sales volume since the event has already occurred in the past. With respect to the example involving the effect of milk promotion on Brand X cookies sales, there is no way to test the theory short of duplicating the milk shortage problem again. Even if the milk shortage problem could be duplicated again for testing purposes, other conditions have changed, including the fact that most consumers who bought milk during that period would not need to or be in a position to buy milk again in a long time. Some factors, such as weather, cannot be duplicated, making theory verification challenging.
Attempts have been made to employ non-aggregate sales data in promoting products. For example, some companies may employ a loyalty card program (such as the type commonly used in grocery stores or drug stores) to keep track of purchases by individual consumers. If an individual consumer has been buying sugar-free cereal, for example, the manufacturer of a new type of whole grain cereal may wish to offer a discount to that particular consumer to entice that consumer to try out the new whole grain cereal based on the theory that people who bought sugar-free cereal tend to be more health conscious and thus more likely to purchase whole grain cereal than the general cereal-consuming public. Such individualized discount may take the form of, for example, a redeemable discount such as a coupon or a discount code mailed or emailed to that individual.
Some companies may vary the approach by, for example, ascertaining the items purchased by the consumer at the point of sale terminal and offering a redeemable code on the purchase receipt. Irrespective of the approach taken, the utilization of non-aggregate sales data has typically resulted in individualized offers, and has not been processed or integrated in any meaningful sense into a promotion optimization effort to determine the most cost-efficient, highest-return manner to promote a particular CPG item to the general public.
Attempts have also been made to obtain from the consumers themselves indications of future buying behavior instead of relying on a backward-looking approach. For example, conjoint studies, one of the stated preference methods, have been attempted in which consumers are asked to state preferences. In an example conjoint study, a consumer may be approached at the store and asked a series of questions designed to uncover the consumer's future shopping behavior when presented with different promotions. Questions may be asked include, for example, “do you prefer Brand X or Brand Y” or “do you spend less than $100 or more than $100 weekly on grocery” or “do you prefer chocolate cookies or oatmeal cookies” or “do you prefer a 50-cent-off coupon or a 2-for-1 deal on cookies”. The consumer may state his preference on each of the questions posed (thus making this study a conjoint study on stated preference).
However, such conjoint studies have proven to be an expensive way to obtain non-historical data. If the conjoint studies are presented via a computer, most users may ignore the questions and/or refuse to participate. If human field personnel are employed to talk to individual consumers to conduct the conjoint study, the cost of such studies tends to be quite high due to salary cost of the human field personnel and may make the extensive use of such conjoint studies impractical.
Further and more importantly, it has been known that conjoint studies are somewhat unreliable in gauging actual purchasing behavior by consumers in the future. An individual may state out of guilt and the knowledge that he needs to lose weight that he will not purchase any cookies in the next six months, irrespective of discounts. In actuality, that individual may pick up a package of cookies every week if such package is carried in a certain small size that is less guilt-inducing and/or if the package of cookies is prominently displayed next to the milk refrigerator and/or if a 10% off discount coupon is available. If a promotion effort is based on such flawed stated preference data, discounts may be inefficiently deployed in the future, costing the manufacturer more money than necessary for the promotion.
Finally, none of the approaches track the long-term impact of a promotion's effect on brand equity for an individual's buying behavior over time. Some promotions, even if deemed a success by traditional short-term measures, could have damaging long-term consequences. Increased price-based discounting, for example, can lead to consumers increasing the weight of price in determining their purchase decisions, making consumers more deal-prone and reluctant to buy at full price, leading to less loyalty to brands and retail outlets.
Previous disclosures by the applicants have focused upon the ability to generate and administer a plurality of test promotions across consumer segments in a rapid manner in order to overcome the foregoing issues in a manner that results in cost-effective, high-return, and timely promotions to the general public. However, there are still remaining issues regarding how to best generate the initial promotions. Previously, ad managers have often relied upon intuition and historical activity to generate the promotions presented to the users. Such systems, even when able to rapidly generate and deploy numerous advertising campaigns often result in missed opportunity since the initial design constraints put forth by a user is less than ideal.
It is therefore apparent that an urgent need exists for systems and methods that enable a user to generate advertisement designs within a visually intuitive design matrix, and with an automated recommendation overlay in order to assist in advertisement selection.